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Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
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Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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Ray B, Chen J, Fu Z, Suresh P, Thapaliya B, Farahdel B, Calhoun VD, Liu J. Replication and Refinement of Brain Age Model for adolescent development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553472. [PMID: 37645839 PMCID: PMC10462059 DOI: 10.1101/2023.08.16.553472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. Our recent study has demonstrated that conventional machine learning models can achieve high accuracy on brain age prediction during development using only a small set of selected features from multimodal brain imaging data. In the current study, we tested the replicability of various brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort. We proposed a new refined model to improve the robustness of brain age prediction. The direct replication test for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age failed precisely estimating brain age variation within a narrow range. The refined model, which combined broad prediction of the pre-trained model and granular information with the narrow age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants' information processing speed and verbal comprehension ability on baseline data.
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Affiliation(s)
- Bhaskar Ray
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
| | - Pranav Suresh
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Britny Farahdel
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
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Basodi S, Raja R, Ray B, Gazula H, Sarwate AD, Plis S, Liu J, Verner E, Calhoun VD. Decentralized Brain Age Estimation Using MRI Data. Neuroinformatics 2022; 20:981-990. [PMID: 35380365 DOI: 10.1007/s12021-022-09570-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age regression models can achieve similar performance compared to the models trained with all the data in one location.
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Affiliation(s)
- Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Rajikha Raja
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Bhaskar Ray
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | | | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA.,Department of Psychology, Georgia State University, Atlanta, GA, USA
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